Inductive Biases   
NAR approaches, which make predictions over edges independently of one-another, have shown strong out-of-distribution generalization for non-sequential problems such as SAT and MVC [1]}.
On the other hand, AR decoders come with the sequential/tour constraint built-in and are the default choice for routing problems [2]}.
Although both approaches have shown close to optimal performance on fixed and small TSP sizes under different experimental settings, it is important to fairly compare which inductive biases are most useful for generalization.
